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Accurate geoid modeling is significant in geodetic, geological, and environmental sciences. Owing to challenges in establishing reference stations, particularly in rugged terrains, such as in Northern Vietnam, leveraging global geopotential models (GGMs) is imperative. Herein, we proposed a superior method that integrates GGMs with advanced artificial intelligence (AI) algorithms to enhance the accuracy and spatial resolution of regional geoid models. A total of six contemporary GGMs (XGM2019e_2159, SGG-UGM-2, SGG-UGM-1, GECO, EIGEN-6C4, and EGM2008) were systematically evaluated to identify the optimal GGM that represents the Earth’s gravitational field in Northern Vietnam. Subsequently, sophisticated AI algorithms, including tree-based ensembles, support vector machines, Gaussian linear regression, regression trees, and linear regression models, were implemented. These AI algorithms were trained on the integrated global navigation satellite system (GNSS) leveling data and corresponding height anomalies to capture complex relationships in the geopotential field. Among the six investigated GGMs, XGM2019e_2159 shows optimal performance for Northern Vietnam, displaying a standard deviation of ±0.17 m. Rigorous assessment results from cross-validation and validation against independent datasets demonstrate satisfactory accuracy across all considered models. However, the Gaussian process regression model with an exponential kernel exhibits marginal superiority, boasting a standard deviation of approximately 0.07 m. This model is therefore chosen for the construction of the geoid model by integrating ground data with optimal GGMs, which shows superior performance, particularly in challenging topographic and geophysical conditions, thereby contributing to a marked improvement in the realized spatial resolution.

Abstract Background Participatory approaches have become a widely applied research approach. Despite their popularity, there are many challenges associated with the evaluation of participatory projects. Here we describe an evaluation of a community-based participatory research study of underserved communities in Ho Chi Minh City (HCMC), Vietnam at risk for hepatitis C virus. The goals of our evaluation were to explore the main benefits and challenges of implementing and participating in a participatory study and to describe study impacts. Methods We conducted two meetings with leaders and members of the participating groups followed by in-depth interviews with 10 participants. We then held a dissemination meeting with over 70 participants, including the representatives of each group, researchers from non-governmental organizations (community-based, national and international), and govenrment officials from the Vietnam Ministry of Health and the Department of Health of HCMC. Results Results include four categories where we describe first the participatory impacts, followed by the collaborative impacts. Then we describe the benefits and challenges of creating and belonging to one of the groups, from members’ and leaders’ points of view. Finally, we describe the key suggestions that participants provided for future research. Conclusion In conclusion, the evaluation approach led to both a research reflection on the ‘success’ of the project and enabled participants themselves to reflect on the outcomes and benefits of the study from their point of view.

Forests play a crucial role in preserving the living environment for humans, but their area is increasingly shrinking under the influence of socio-economic development. This article presents the results of applying remote sensing and GIS to assess forest resource changes in a rural district - Loc Ninh, Binh Phuoc Province, Vietnam. The study used Landsat 5 TM and Landsat 8 OLI images and processed them using ENVI 4.7 and ArcGIS Desktop 10.7 software to create forest distribution maps for the periods 2000, 2010, and 2020. The research results show that the forest area in Loc Ninh rural district experienced significant changes during the 2000-2020 period. The total forest area lost was over 13.4% (8824 ha) compared to 2000. The reasons are explained by the pressure of rapid population growth, remnants of shifting cultivation practices, and the consequences of land use conversion and expansion for production purposes. The research results provide valuable reference materials for forest management and protection in Loc Ninh rural district, Vietnam.

Rivers, such as the Dong Nai, Saigon, and Mekong, are subject to urban, industrial, and agricultural pollution, leading to a high risk of heavy metal accumulation in bottom sediments. With the aid of a boat and bucket as tools, a survey was conducted to collect sediment samples from the coastal areas (downstream of Dong Nai, Saigon, Mekong Rivers) and islands (Con Dao, Tho Chu) in the Southern region of Vietnam during April and May 2023. Out of a total of 35 samples collected, preserved according to the guidelines of the Ministry of Natural Resources and Environment, the heavy metal indicators were analyzed by atomic absorption spectroscopy, and the average heavy metal concentrations were in the following order: Zn>Cu>Cd>Pb>As. Compared with the Vietnamese Technical Regulation (QCVN 43:2017/BTNMT) on sediment quality and the sediment quality of the Canadian Environment Ministry, the heavy metal concentrations in the research area have not exceeded the specified standards. However, compared with standards from the USA, the Russian Federation, and other regions in Vietnam, the levels are quite high. Moreover, the Igeo index indicates that the concentration of Pb, As, and Cd at many coastal locations downstream of the rivers is heavily polluted. This evidences that in addition to pollution from upstream sources, the activities of the local population significantly contribute to the accumulation of pollution. The research results provide a scientific basis for limiting pollution sources to ensure sustainable development of the studied areas, which is necessary today.

Abstract Muscle ultrasound has been shown to be a valid and safe imaging modality to assess muscle wasting in critically ill patients in the intensive care unit (ICU). This typically involves manual delineation to measure the rectus femoris cross-sectional area (RFCSA), which is a subjective, time-consuming, and laborious task that requires significant expertise. We aimed to develop and evaluate an AI tool that performs automated recognition and measurement of RFCSA to support non-expert operators in measurement of the RFCSA using muscle ultrasound. Twenty patients were recruited between Feb 2023 and July 2023 and were randomized sequentially to operators using AI (n = 10) or non-AI (n = 10). Muscle loss during ICU stay was similar for both methods: 26 ± 15% for AI and 23 ± 11% for the non-AI, respectively (p = 0.13). In total 59 ultrasound examinations were carried out (30 without AI and 29 with AI). When assisted by our AI tool, the operators showed less variability between measurements with higher intraclass correlation coefficients (ICCs 0.999 95% CI 0.998–0.999 vs. 0.982 95% CI 0.962–0.993) and lower Bland Altman limits of agreement (± 1.9% vs. ± 6.6%) compared to not using the AI tool. The time spent on scans reduced significantly from a median of 19.6 min (IQR 16.9–21.7) to 9.4 min (IQR 7.2–11.7) compared to when using the AI tool (p

The burgeoning field of pharmacomicrobiomics offers promising insights into the intricate interplay between the microbiome and cancer, shaping responses to diverse treatment modalities. This review aims to analyze the molecular mechanisms underlying interactions between distinct microbiota types and cancer, as well as their influence on treatment outcomes. We explore how the microbiome impacts antitumor immunity, and response to chemotherapy, immunotherapy, and radiation therapy, unveiling its multifaceted roles in cancer progression and therapy resistance. Moreover, we discuss the challenges hindering the development of microbiome-based interventions in cancer therapy, including standardization, validation, and clinical translation. By synthesizing clinical evidence, we underscore the transformative potential of harnessing pharmacomicrobiomics in guiding cancer treatment decisions, paving the way for improved patient outcomes in clinical practice.

Objectives The associations between the neutrophil-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) with the responses of non-small cell lung cancer (NSCLC) patients receiving immune checkpoint inhibitors (ICI) and the NLR/PLR predictive potential were evaluated via meta-analysis. Methods A systematic review was conducted using the PubMed, Embase, and The Cochrane Library databases until October 2021. The relationship between NLR/PLR and overall survival (OS) and progression-free survival (PFS) was evaluated using pooled hazard ratios (HR). The relationship between NLR/PLR and overall response rate (ORR) and disease control rate (DCR) was assessed via pooled odds ratios (OR). Heterogeneity between studies, publication bias, subgroup and sensitivity analyses, trim and fill meta-analysis, and the contour-enhanced funnel plot were performed using the R software. Results A total of 44 (out of 875) studies met the eligibility criteria, providing a sample size of 4597 patients. Patients with a high NLR were statistically significantly associated with worse outcomes, including OS (pooled HR = 2.44; P < 0.001), PFS (pooled HR = 2.06; P < 0.001), DCR (pooled OR = 0.71; P < 0.001), and ORR (pooled OR = 0.33; P < 0.001). Similarly, a high PLR was associated with poorer outcomes in response to ICI drugs, including OS (pooled HR = 2.13; P < 0.001) and PFS (pooled HR = 1.61; P < 0.001). Conclusion High NLR and PLR were associated with a statistically significant reduction in the efficacy of ICI drugs in NSCLC patients. Thereby, it is possible to use NLR and PLR as potential and available biomarkers in the clinical practice to predict the outcome of ICI treatment in NSCLC patients.

Mức độ đa dạng loài của chi Baccaurea thường gây khó khăn cho công tác bảo tồn, chế biến và ứng dụng trong y học. Đề tài được thực hiện nhằm mục tiêu ghi nhận kết quả mô tả, so sánh đặc điểm cấu tạo của lá, hoa và trái ở bốn giống dâu Hạ Châu, dâu Xiêm, dâu Xanh và dâu Vàng được thu tại huyện Phong Điền dựa trên phương pháp phân tích hình thái thực vật. Kết quả mô tả cho thấy hình thái lá của bốn giống dâu da trong nghiên cứu mang đặc điểm chung của chi Baccaurea. Diện tích phiến lá, số gân lá của dâu Hạ Châu và dâu Xiêm có sự tương đồng và đều lớn hơn hai giống dâu còn lại. Màu sắc và hình dạng trái của bốn giống dâu da có sự khác biệt rõ rệt dễ dàng nhận thấy bằng mắt thường. Không tìm thấy sự khác biệt về thành phần và số lượng trong cấu trúc hoa nhưng số lượng hoa trên mỗi phát hoa thì khác nhau. Phân tích cấu trúc giải phẫu cũng thấy có sự đa dạng giữa các giống dâu. Kiểu gốc lá, cấu trúc lông đơn bào có ở dưới phiến và cuống lá là điểm khác biệt dùng để nhận diện dâu Hạ Châu.

Kon Plông là một trong những khu vực có giá trị đa dạng sinh học và bảo tồn quan trọng của Việt Nam. Bảo tồn rừng bằng cách trồng cây dược liệu dưới tán rừng được xem là hướng đi phù hợp. Quy trình thực hiện và các tiêu chí lựa chọn cây thuốc có thể trồng dưới tán rừng đã được xây dựng. Việc khảo sát, lập danh mục các loài cây làm thuốc ở các sinh cảnh rừng đã được tiến hành. Qua kết quả khảo sát, 170 loài thực vật có khả năng làm thuốc dưới tán rừng đã được ghi nhận. Bằng các đánh giá về mức độ đa dạng như dạng thân, công dụng, bảy loài gồm lan kim tuyến (Anoectochilus setaceus), hương bài (Dianella ensifolia), rau lủi (Gynura sp.), sâm dây (Codonopsis javanica), giảo cổ lam (Gynostemma pentaphyllum), tiêu rừng (Litsea cubeba) và chè dây (Ampelopsis cantoniensis) đã được chọn làm nhóm cây thuốc tiềm năng. Cấu trúc giải phẫu và một số hợp chất hóa học cũng đã được khảo sát để để bổ sung cơ sở dữ liệu cho các loài này.

This study explores the problem of maximizing the sum rate in uplink multi-user Multiple-Input Multiple-Output (MIMO) using Rate-Splitting Multiple Access (RSMA) systems. The investigation revolves around the scenario where the Users (UEs) are single-antenna nodes transmitting data to a multi-antenna Base Station (BS) through the RSMA technique. The optimization process encompasses determining parameters such as UEs’ transmit powers, decoding order, and detection vector at the BS. An approach based on Deep Reinforcement Learning (DRL) is introduced to address this challenge. This DRL framework involves an action-refined stage and applies a Deep Deterministic Policy Gradient (DDPG)-based strategy. Simulation outcomes effectively demonstrate the convergence of the proposed DRL framework, where it converges after approximately 1,800 episodes. Also, the results prove the superior performance of the proposed method when compared to established benchmark strategies, where it is up to 45% and 86% higher than the local search and random schemes, respectively.

Financial knowledge plays a vital role in economic decisions. However, research on overconfidence or underconfidence in financial knowledge is limited. Therefore, the study examines these behavioral biases. Using data on 210 individual investors with three methods of multivariate regression, structural equation model, and logit regression, the results show that over- and under-confidence in financial knowledge directly affect the trading frequency and indirectly affect the use of debt. The results have implications for policymakers in raising investor awareness, helping to avoid behavioral biases in financial decisions, and contributing to sustainable stock market development.

This study assesses the impact of the Global Minimum Tax (GMT) on the attraction of foreign direct investment (FDI) in Vietnam. The authors rely on the analytical framework of UNCTAD (2022) and descriptive statistical methods to analyze and evaluate the impact of GMT on FDI attraction. The research findings indicate that the implementation of GMT will affect tax competitiveness, profit shifting, investment location, and investment scale in various degrees and directions. In particular, GMT may create conflicts between current tax laws and GMT rules, leading to inconsistencies and difficulties in enforcing tax regulations. Vietnam will have to compete with other countries in asserting tax jurisdiction over investment activities on its territory. GMT may negatively affect FDI inflows into Vietnam in sectors with a high tax elasticity to investments. Vietnam may be forced to move away from competition for attracting FDI solely based on corporate income tax and shift to new forms of competition. Based on these findings, the paper proposes several recommendations for the Vietnamese government to refine policies related to GMT.

Phosphorus pollution poses a significant environmental challenge in wastewater from industries like modified starch production. This study investigates the feasibility of mitigating phosphorus contamination in modified starch production wastewater using calcium and magnesium ions. In the first step, polyaluminum chloride (PAC) alone achieves a phosphorus removal efficiency of 38 % with a dosage of 800 mg/L. Subsequently, in the second step, optimal conditions are achieved at a Ca:P molar ratio of 2.5–3 with a 20-min reaction time, resulting in a remarkable phosphorus removal efficiency of 97 %. The study highlights the critical role of pH adjustment during precipitation reactions and offers insights into efficient phosphorus removal strategies. These findings contribute to sustainable wastewater treatment methodologies, addressing environmental concerns in industrial settings.

This study investigates the impact of weather information sources on consequences associated with extreme heat events, employing a cross-sectional survey conducted on hospitalized individuals who have experienced heat stress in Central Vietnam. Multiple information channels, including official agencies and social media, were found to significantly reduce the Cost of Illness (COI) associated with heat-related ailments. Social media, in particular, emerged as a potent tool for climate adaptation. Improving the clarity and accessibility of weather information through official channels is crucial, especially for households with varying income levels. Demographic factors such as age and gender should be considered when fine-tuning communication strategies, with special attention given to individuals with underlying medical conditions, who are particularly susceptible to extreme heat effects. These findings underscore the need to maximize the reach of weather-related information and reduce economic burdens on affected populations. This provides valuable insights for policymakers aiming to bolster climate resilience in vulnerable regions like Vietnam, emphasizing the significance of diverse information sources and tailored communication in climate change adaptation.

A combined slab and cluster model approach is suitable for obtaining accurate estimates of reaction and activation energies for surface processes. This approach for Si(100)/H(sub 2) system would be useful to provide a reliable, consistent set of data for later modeling of kinetics.

Forests serve as vital ecosystems, playing a pivotal role in preserving biodiversity, maintaining air and water quality, and regulating the climate, all while delivering many benefits to humanity and serving as a critical resource for diverse industries. This study investigated changes in forest cover within Yok Don National Park and surrounding areas in Dak Lak province, Vietnam, over the period from 2001 to 2021. The primary objective was to identify decadal shifts in forest cover through the utilization of freely accessible satellite remote sensing data and geographic information systems (GIS) tools. The maximum likelihood classification technique was employed to categorize Landsat images captured in 2001, 2011, and 2021. Furthermore, we evaluated the efficacy of the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI) based classifications in detecting forest cover changes when compared to supervised classification. Initial estimates placed forest cover at 2796.91 km2, constituting 88.44% of the total land area in 2001, which subsequently declined to 2609.97 km2 (82.53%) in 2011, and further reduced to 1843.57 km2 (58.29%) in 2021. Severely reduced forest cover is often associated with the expansion of agriculture on the forest edge; other contributing factors include logging and illegal production land. Notably, the NDVI outperformed the SAVI in detecting forest cover changes. The findings of this research are anticipated to contribute to ongoing studies and enhance the effective monitoring of this protected forest area, thus providing invaluable insights for decision-makers.

Density functional theory and a semi-empirical quantum chemical approach were used to evaluate the photocatalytic efficiency of ceria (CeO2) combined with reduced graphene oxide (rGO) and graphene (GP) for degrading methylene blue (MB). Two main aspects were examined: the adsorption ability of rGO and GP for MB, and the separation of photogenerated electrons and holes in CeO2/rGO and CeO2/GP. Our results, based on calculations of the adsorption energy, population analysis, bond strength index, and reduced density gradient, show favorable energetics for MB adsorption on both rGO and GP surfaces. The process is driven by weak, non-covalent interactions, with rGO showing better MB adsorption. A detailed analysis involving parameters like fractional occupation density, the centroid distance between molecular orbitals, and the Lewis acid index of the catalysts highlights the effective charge separation in CeO2/rGO compared to CeO2/GP. These findings are crucial for understanding photocatalytic degradation mechanisms of organic dyes and developing efficient photocatalysts.

Globally, coastal cultivation regions (CCRs) are facing irrigation water shortages due to the impacts of climate change (ICV). CCRs have repeatedly suffered from saline irrigation water, increases in temperature, and reduced rainfall, resulting in irrigation water shortages. The aim of this study was, therefore, to evaluate the impacts of weather variables on the rice water requirement (RWR) in CCRs in Kien Giang Province. The response of rice grain yield to weather variables was estimated by simulating the AquaCrop model under different crop cultivation schedules (CCSs). These schedules involved advancing or delaying the sowing date by 30 days compared to the current common sowing date (baseline) for winter-spring (WS) and summer-fall (SF) vegetation seasons. The AquaCrop model demonstrated accurate simulation capabilities, as indicated by the high correlation values of statistical error indexes during the validation and calibration procedures. The findings revealed that rice grain yields would enhance up to 7.8% and 5.6% compared to the baseline when CCSs involve sowing 20 days earlier for WS vegetation season and are advanced by 20 days for SF vegetation season. Overall, modifying CCSs for coastal rice paddies in the study area, while considering weather variables, would have a positive contribution by mitigating the adverse effects of ICV.

Theo kinh nghiệm dân gian, cây an xoa (Helicteres hirsuta Lour.) có nhiều hoạt chất sinh học đặc biệt, có khả năng kháng oxy hóa, bảo vệ gan, kháng lại nhiều loại tế bào ung thư. Nghiên cứu này đã tiến hành khảo sát các đặc điểm thực vật học, thành phần hóa học và đặt tính đất, của H. hirsuta thu tại hai địa điểm: núi Cấm (An Giang) và Lại Sơn (Kiên Giang). Mục tiêu của nghiên cứu xây dựng dữ liệu cho việc xác định hiệu quả điều trị bệnh và khoanh vùng các khu vực trồng an xoa tại đồng bằng sông Cửu Long. Kết quả cho thấy, thành phần dinh dưỡng đất (đặc biệt là đạm và lân) có ảnh hưởng đến hình thái và cấu trúc giải phẫu của loài, tuy nhiên, an xoa là nhóm thực vật có nhu cầu dinh dưỡng thấp theo thang đánh giá của TCVN (2000). Có sự khác biệt về thành phần hóa học khi định tính một số hợp chất tại các khu vực thu mẫu khác nhau, một số chất dược liệu đặc biệt như saponin, phenol và coumarin chỉ có ở mẫu thu được tại Lại Sơn.

Dengue shock (DS) is the most severe complication of dengue infection; endothelial hyperpermeability leads to profound plasma leakage, hypovolaemia and extravascular fluid accumulation. At present, the only treatment is supportive with intravenous fluid, but targeted endothelial stabilising therapies and host immune modulators are needed. With the aim of prioritising potential therapeutics, we conducted a prospective observational study of adults (≥16 years) with DS in Vietnam from 2019-2022, comparing the pathophysiology underlying circulatory failure with patients with septic shock (SS), and investigating the association of biomarkers with clinical severity (SOFA score, ICU admission, mortality) and pulmonary vascular leak (daily lung ultrasound for interstitial and pleural fluid). Plasma was collected at enrolment, 48 hours later and hospital discharge. We measured biomarkers of inflammation (IL-6, ferritin), endothelial activation (Ang-1, Ang-2, sTie-2, VCAM-1) and endothelial glycocalyx breakdown (hyaluronan, heparan sulfate, endocan, syndecan-1). We enrolled 135 patients with DS (median age 26, median SOFA score 7, 34 required ICU admission, 5 deaths), together with 37 patients with SS and 25 healthy controls. Within the DS group, IL-6 and ferritin were associated with admission SOFA score (IL-6: βeta0.70, p

Abstract Background Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited training available. Despite recent advances in the use of Artificial Intelligence (AI) to automate many ultrasound imaging analysis tasks, no AI-enabled LUS solutions have been proven to be clinically useful in ICUs, and specifically in LMICs. Therefore, we developed an AI solution that assists LUS practitioners and assessed its usefulness in a low resource ICU. Methods This was a three-phase prospective study. In the first phase, the performance of four different clinical user groups in interpreting LUS clips was assessed. In the second phase, the performance of 57 non-expert clinicians with and without the aid of a bespoke AI tool for LUS interpretation was assessed in retrospective offline clips. In the third phase, we conducted a prospective study in the ICU where 14 clinicians were asked to carry out LUS examinations in 7 patients with and without our AI tool and we interviewed the clinicians regarding the usability of the AI tool. Results The average accuracy of beginners’ LUS interpretation was 68.7% [95% CI 66.8–70.7%] compared to 72.2% [95% CI 70.0–75.6%] in intermediate, and 73.4% [95% CI 62.2–87.8%] in advanced users. Experts had an average accuracy of 95.0% [95% CI 88.2–100.0%], which was significantly better than beginners, intermediate and advanced users (p

Antibiotic resistant bacteria (ARB) in aquatic environments is attracting increasing attention. However, the spread of ARB along Perfume River in Hue City is poorly understood. This study aimed to phenotypically and genotypically characterize β-lactam- and fluoroquinolone-resistant Escherichia coli isolates from this river. Water samples were collected from the urban, rural, agricultural, and less-affected areas in March 2020. E. coli susceptibility to seven commonly employed antibiotics was analyzed using the disk diffusion method, and the antibiotic resistance genes (ARGs), qnrA, qnrB, qnrS, TEM, SHV, and CTX-M, were identified using polymerase chain reaction (PCR) and DNA sequencing. The antibiotic susceptibility patterns of E. coli revealed that the rate of amoxicillin resistance was the highest (60%). PCR assays and sequencing of 12 β-lactam-resistant E. coli isolates indicated the presence of blaTEM and blaCTX-M-15 in 58.3% and 16.7% of the isolates, respectively. Only one of four fluoroquinolon -resistant E. coli isolates harbored the qnrS, while qnrA or qnrB genes were not detected. These findings suggest that this water may be an essential source of transmissible ARGs in Hue City, which may have a detrimental impact on the people living in this area.